Development of Synthetic Drive Cycle for Fuel Economy Prediction 2012-01-2048

With growing fuel prices and global warming, fuel economy improvement and reduced emissions are becoming order of the day. Automobile manufacturers around the world are in increasing pressure to achieve the same, also keeping in account the stiff timelines required in the product developmental cycle. Condensed duty cycle that is representative of several days of actual real life running is developed for quicker fuel economy tuning on a chassis dynamometer. This paper presents a new methodology to obtain a synthetic drive duty cycle, which matches engine operating conditions of the actual real life cycle accurately and thereby providing a more accurate match in fuel economy.

Drive duty cycle (vehicle velocity profile) used in this study is extracted from the instrumented vehicle in the real traffic condition (peak/lean hour) of major cities in India at different location/load conditions. Each recorded trip is further divided into micro trips. Several principal components such as average velocity, percentage of times in acceleration, deceleration, cruise & idle, shift pattern closeness, (which is a measure of number of shifts from one of the above-mentioned state to the other), etc., of the entire trip and individual micro trips are evaluated. The synthetic drive duty cycle is derived by minimizing the objective function, which is created from a selected set of the principal components. An optimization algorithm that reduces the objective function coded in MATLAB is used to create the synthetic duty cycle by suitably clubbing the micro trips such that there are no repetitions. The synthetic cycle and the full trip data are both analyzed for fuel economy in AVL-CRUISE software. From the AVL-CRUISE simulation results, fuel economy & engine operating points of condensed & real-world cycle are compared. Principal components that yield the closest match not only in fuel economy but also on engine operating points repeatedly are identified and presented.

With the identified objective function from a optimal set of principal components, the engine operating point plots and hence the fuel economy values matches very well with that of the entire trip to within ±3% and the total development time is reduced to less than 1/6th of the time consumed otherwise. This methodology provides repeatable & reproducible results consistently. Some of the factors affecting the data obtained from different cycles are discussed and possible methodologies to develop synthetic cycles across different vehicle categories/platforms are suggested.